ee.Kernel.roberts

  • Creates a 2x2 kernel used for Roberts edge detection, a method for identifying edges in images by approximating the gradient.

  • The kernel's values can be scaled using the magnitude parameter and normalized to sum to 1 using the normalize parameter.

  • The default kernel has the values [[1, 0], [0, -1]], which represent the weights applied to neighboring pixels to calculate the edge strength.

 Generates a 2x2 Roberts edge-detection kernel.

UsageReturns
ee.Kernel.roberts(magnitude, normalize)Kernel
ArgumentTypeDetails
magnitudeFloat, default: 1Scale each value by this amount.
normalizeBoolean, default: falseNormalize the kernel values to sum to 1.

Examples

Code Editor (JavaScript)

print('A Roberts kernel', ee.Kernel.roberts());

/**
 * Output weights matrix; center is position [1,1]
 *
 * [1,  0]
 * [0, -1]
 */

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

from pprint import pprint

print('A Roberts kernel:')
pprint(ee.Kernel.roberts().getInfo())

#  Output weights matrix; center is position [1,1]

#  [1,  0]
#  [0, -1]